Impact of Edge Detection Algorithms on Different Types of Images using PSNR and MSE

Edge detection is the process of detecting sharp changes in image brightness in a digital image. It aids in the recognition of an object and its shape in an image. As a result, edge detection plays a vital role in image processing, especially in domains like segmentation, image registration, and object identification. This paper is an attempt to study the impact of several edge detection algorithms such as Sobel, Prewitt, Robert, Kirsch, Robinson, Laplacian of Gaussian (LOG) and Canny. The three different types of images such as medical, natural and satellite images are considered for experiment. Performance measures used for comparison are Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR)